If the point of the ResNet skip connection is to let the main path learn the residual relative to identity, why are there convolutional skips?
Well, I found an answer that satisfies me.

The zero-padded identity is not ideal. Suppose we're mapping from 64 channels to 128 channels. Then the zero-padded identity will map to an output where half of the channels are the same as the inputs, and the other half are all zeros. So that means the main path is learning a residual for half of the output channels, and learning a mapping from the ground up for the other half of the channels.

Now, the alternative is to use option (B) which is the single 1x1 convolution. After reading their ResNetV2 paper I realised that they really double down on this concept of maintaining a "clean" alternative path all the way up and down the network. So then the question is, what's "cleaner"?: a block of two convolutions of the form 3x3x64 then 3x3x128, or a shortcut with a 1x1x128 convolution? The shortcut is a tempting choice here. And in fact, in the original paper they show empirically that the convolutional shortcut is better than the identity shortcut. Maybe it might be worth running a test to see if the convolutional shortcut is better than no shortcut at all, but until I decide to run such a test, I'll presume the authors would have mentioned it. The other thing worth noting is that there are only a few places in the whole network where the dimensions need to be increased, so maybe the impact is small.